where \(\eta _1\) , \(\eta _2\) , and \(\eta _3\)  are the Lagrange multipliers and we use \((\cdot )^+\)  to denote \(\max (\cdot , 0)\)  which truncates the negative values to zero. After sampling candidate subgoal sequences in the lossy representation space, we evaluate each candidate using the cost function. Model predictive path integral (MPPI) [1]} is used to iteratively optimize the plan through importance sampling.
